An ambispective, multi-center, observational study to develop and validate AI Model to assist orthopaedic surgeons in the precise selection of sizes of nails, screws, and implants.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 100,000
- 试验地点
- 1
研究概览
简要总结
Fracture management, particularly internal fixation using implants such as intramedullary nails, plates, and screws, is a critical aspect of orthopaedic surgery. Selecting the appropriate implant type, size, and configuration is often a complex and subjective process, influenced by fracture type, location, patient age, bone density, and fracture dimensions. Incorrect implant selection can lead to complications, including non-union, malunion, and implant failure. Therefore, this study aims to develop and validate an AI-based decision support system for recommending the most appropriate fixation devices and their sizes (nails, screws, and implants) for orthopedic fracture fixation. The system will utilize structured clinical and radiographic data, including fracture size, location, classification, and relevant patient parameters. The AI model will recommend optimal fixation strategies based on data sourced from patient populations, ensuring that it accounts for both clinical and imaging insights.
The study includes both retrospective and prospective arms for the research, and it involves patient data, including imaging and clinical data. In the retrospective arm, past patient cases are used to train and validate the AI model, while the prospective arm involves enrolling new fracture patients, collecting imaging and clinical data, and utilizing AI for implant recommendations.
Given that the study involves human data both retrospective (past cases) and prospective (new fracture patients), this trial will be conducted on human beings. This is based on the involvement of patient data, both historical (retrospective) and prospective (new patients), which includes human clinical and imaging data for the purposes of AI model training, validation, and outcome assessments.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18.00 Year(s) 至 99.00 Year(s)（—）
- 性别
- All
入选标准
- •Retrospective arm:
- •Patients greater than equal to 18 years who underwent internal fixation of bone fractures in the past 2 years or more.
- •Availability of pre-operative, intra-operative and post-operative imaging (X-ray with/without CT scans) with acceptable quality for fracture classification and size estimation.
- •Complete surgical records detailing the type, size, and configuration of implants (Meril Healthcare Pvt.
- •Availability of post-operative and follow-up data including union status, complications, or revision surgery outcomes.
- •Prospective arm:
- •Patients greater than equal to 18 years presenting with acute fractures of bones requiring internal fixation with nails, screws, or plates.
- •Availability of digital imaging (X-ray ± CT) suitable for annotation and AI processing.
排除标准
- •Retrospective arm:
- •Pathological fractures (e.g., metastatic, cystic, or osteoporotic collapse without trauma).
- •Polytrauma patients with incomplete imaging or missing clinical/surgical records.
- •Previous surgery on the same bone that alters normal anatomy or complicates implant selection.
- •Inadequate imaging quality for AI analysis or missing calibration for measurement.
- •Prospective arm:
- •Pathological fractures or periprosthetic fractures.
- •Pediatric patients (less than 18 years) due to differing implant selection protocols.
- •Patients with incomplete imaging or poor-quality scans that do not allow proper annotation.
- •Patients refusing consent for participation in the prospective arm.
研究者
Dr Bhaumik Dave
Nuvo AI Pvt. Ltd